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SpatialVx (version 1.0-3)

Spatial Forecast Verification

Description

Spatial forecast verification refers to verifying weather forecasts when the verification set (forecast and observations) is on a spatial field, usually a high-resolution gridded spatial field. Most of the functions here require the forecast and observed fields to be gridded and on the same grid. For a thorough review of most of the methods in this package, please see Gilleland et al. (2009) and for a tutorial on some of the main functions available here, see Gilleland (2022) .

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Version

Install

install.packages('SpatialVx')

Monthly Downloads

539

Version

1.0-3

License

GPL (>= 2)

Maintainer

Last Published

November 26th, 2024

Functions in SpatialVx (1.0-3)

Threshold-based Feature Finder

Elmore, Baldwin and Schultz Method for Field Significance for Spatial Bias Errors

Force Merges in Matched Feature Objects

ExampleSpatialVxSet

Simulated Spatial Verification Set

Major and Minor Axes of a Feature

Forecast Verification with Cluster Analysis: The Variation

Temporal Block Bootstrap Keeping Locations in Space Constant

Raw Image Moments.

Connectivity Index

2-d Interpolation

Example Verification Set

Example Precipitation Rate Verification Set (NIMROD)

FeatureMatchAnalyzer

Analyze Features of a Verification Set

Single Feature Properties

Loss functions for the spatial prediction comparison test (SPCT)

SpatialVx-package

Spatial Forecast Verification

Monte Carlo Degrees of Freedom

Variograms for a Gridded Verification Set

Bearing from One Spatial Location to Another

CircleHistogram

Circular Histogram

Create Binary Fields

Feature-Based Contingency Table

Spatial Forecast Verification Shape Analysis

The Spatial Alignment Summary Measure Called G

Centroid Distance Between Two Identified Objects

Binary Image Measures

Cluster Analysis Verification

Spatial Forecast Verification Methods Inter-Comparison Project (ICP) Test Cases and other example verification sets

Spatial-Alignment Summary Measures

Geographic Box Plot

Localization Performance Measures

SpatialVx-internal

Internal functions for the SpatialVx package

Optical Flow Verification

Identify Disjoint Sets of Connected Components

calculate_FSSvector_from_binary_fields

Calculate FSS Values

Combine Features/Matched Objects

Create Composite Features

Neighborhood Verification Statistics for a Gridded Verification Set.

Merge and/or Match Identified Features Within Two Fields

Quilt Plot and a Matrix Plot.

S1 Score, Anomaly Correlation

Calculate dFSS Score Value

Exponential Variogram

Feature Interest

Centered on Square Domain Baddeley's Delta Metric

Binary Location Metric Proposed in Zhu et al. (2011)

Field Significance Method of Elmore et al. (2006)

Spatial Prediction Comparison Test

Some Common Traditional Forecast Verification Statistics.

Exponential Variogram

Minimum Boundary Separation Feature Matching

calculate_FSSwind

Calculate FSSwind Score Value

Create Several Graphics for List Objects Returned from hoods2d

Create Surrogate Fields

Upscaling Neighborhood Verification on a 2-d Verification Set

Test for Equal Predictive Ability on Average Over a Regularly Gridded Space

Various Verification Statistics on Possibly Neighborhood-Smoothed Fields.

Image Warping By Hand

Variography Score

Practically Perfect Hindcast Neighborhood Verification Method

Apply a Threshold to a Field

Spatial Verification Sets -- SpatialVx Object

Intensity Scale (IS) Verification

Apply Traditional Forecast Verification After Wavelet Denoising

Rigid Transformation

2-d Gaussian Mixture Models Verification

Feature-based Analysis of a Field (Image)

Simulated Forecast and Verification Fields

Structure Function for Non-Gridded Spatial Fields.

High-Resolution Gridded Forecast Verification Using Discrete Wavelet Decomposition

structurogram.matrix

Structure Function for Gridded Fields

Forecast Quality Index